Joint Optimization of Task Offloading and Resource Allocation based on Edge-terminal Collaboration

Jing Bai, Xu Liu, Xiaorong Zhu, Yiming Wu
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Abstract

With the development of the Internet of Things (IoT), the number of mobile terminals with sensing, communication and computing (SCC) capabilities has exploded. Mobile terminals sense the task data from physical environment through cameras, sensors, etc. Then process the generated task by local computing or offloading to the edge computing node, So that the mobile terminal can make better decisions in the next time slot. In this paper, a joint optimization algorithm of task offloading and sensing-communication-computing resource allocation based on edge-terminal collaboration is proposed. Firstly, a “sensing-then-processing” protocol is adopted to coordinate the data sensing, communication and computing. A joint optimization problem of joint task offloading and resource allocation is presented for maximizing the amount of data processed by the system. Then the non-convex optimization problem is transformed into a tractable convex approximation problem, and the suboptimal solution is obtained based on a successive convex approximation (SCA) algorithm. Finally, simulation results show that proposed algorithm can effectively improve the system processing capability.
基于边缘终端协作的任务卸载与资源分配联合优化
随着物联网(IoT)的发展,具有传感、通信和计算(SCC)功能的移动终端数量呈爆炸式增长。移动终端通过摄像头、传感器等感知来自物理环境的任务数据。然后将生成的任务通过本地计算或卸载到边缘计算节点进行处理,以便移动终端在下一个时隙做出更好的决策。提出了一种基于边缘终端协同的任务卸载与传感-通信-计算资源分配联合优化算法。首先,采用“先感知后处理”协议协调数据感知、通信和计算;为使系统处理的数据量最大化,提出了联合任务卸载和资源分配的联合优化问题。然后将非凸优化问题转化为可处理的凸逼近问题,并基于逐次凸逼近(SCA)算法得到次优解。仿真结果表明,该算法能有效提高系统的处理能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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